Training a multi-class image classifier requires a large number of unlabeled images. Intelligently exploiting a large number of images is a challenging problem. Active training (often called active learning) aims to select informative images to train classifiers for binary and multi-class classification. Even though multi-class active training methods successfully reduce the number of training images required, they can be labor intensive from a user interaction standpoint for the following reasons:                (i) for each unlabeled image queried for labeling, the user has to sift through many classes to input the precise one. Especially for images, providing input in this form can be difficult, and sometimes impossible when a huge (or unknown) number of classes are present;        (ii) the time and effort required increase with an increase in the number of classes;        (iii) the user interaction is prone to mistakes in labeling, and        (iv) it is not easily amenable to distributed labeling because all users need to be consistent in the labeling.        
Databases of images are ever increasing in size and the image variety. It is common to have thousands of image classes. In order to design methods that are practical at larger scales, it is essential to allow easier modes of labeling and interaction for the user.